US20180240071A1

Movatterモバイル変換

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Publication number: US20180240071A1
Application number: US15/437,649
Authority: US
Inventors: Krishnaram Kenthapadi; Kaushik Rangadurai
Original assignee: Microsoft Technology Licensing LLC
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2017-02-21
Filing date: 2017-02-21
Publication date: 2018-08-23
Also published as: US10783497B2

Abstract

Methods, systems, and computer programs are presented for searching jobs for a member of a social network based on transitions of workers between companies. One method includes an operation for identifying jobs based on a search for jobs for the member who works for an employer, with each job being associated with a respective company. For each company associated with the jobs, the method determines an intercompany migration score indicating a transition probability that a coworker transitions from the employer to the company. For each job, a job affinity score is determined based on a comparison of job data and a member profile. Further, the method includes operations for ranking the jobs based on the intercompany migration score of the company and the job affinity score, and for causing presentation of a group including one or more of the ranked jobs in a user interface based on the ranking.

Description

TECHNICAL FIELD

The subject matter disclosed herein generally relates to methods, systems, and programs for finding quality job offerings for a member of a social network.

BACKGROUND

Some social networks provide job postings to their members. The member may perform a job search by entering a job search query, or the social network may suggest jobs that may be of interest to the member. However, current job search methods may miss valuable opportunities for a member because the job search engine limits the search to specific parameters. For example, the job search engine may look for matches of a job in the title to the member's title, but there may be quality jobs that are associated with a different title that would be of interest to the member.

Further, existing job search methods may focus only on the job description or the member's profile, without considering the member's preferences for job searches that go beyond the job description or other information that may help find the best job postings for the member.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope.

FIG. 1 is a block diagram illustrating a network architecture, according to some example embodiments, including a social networking server.

FIG. 2 is a screenshot of a user interface that includes job recommendations, according to some example embodiments.

FIG. 3 is a screenshot of a user's profile view, according to some example embodiments.

FIG. 4 is a diagram of a user interface, according to some example embodiments, for presenting job postings to a member of a social network.

FIG. 5 is a detail of an intercompany-migration group area in the user interface ofFIG. 4, according to some example embodiments.

FIG. 6 illustrates data structures for storing job and member information, according to some example embodiments.

FIGS. 7A-7B illustrate the scoring of a job for a member, according to some example embodiments.

FIG. 8 illustrates the training and use of a machine-learning program, according to some example embodiments.

FIG. 9 illustrates the transitions of members between companies, according to some example embodiments.

FIG. 10 illustrates a method for selecting jobs for presentation within a group, according to some example embodiments.

FIG. 11 illustrates a social networking server for implementing example embodiments.

FIG. 12 is a flowchart of a method, according to some example embodiments, for searching job postings for a member of a social network based on the transitions of members between companies.

FIG. 13 is a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments.

FIG. 14 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

Example methods, systems, and computer programs are directed to searching job postings for a member of a social network based on the transitions of members between companies. Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details.

One of the goals of the present embodiments is to personalize and redefine how job postings are searched and presented to job seekers. Another goal is to explain better why particular jobs are recommended to the job seekers. The presented embodiments provide both active and passive job seekers with valuable job recommendation insights, thereby greatly improving their ability to find and assess jobs that meet their needs.

Instead of providing a single job recommendation list for a member, embodiments presented herein define a plurality of groups, and the job recommendations are presented within the groups. Each group provides an indication of a feature that is important to the member for selecting from the group, such as how many people have transitioned from the company of the member to the company offering the job, who would be a virtual team for the member if the member joined the company, and the like.

Embodiments presented herein analyze data regarding transitions of members of the social network from one company to another. This way, if a company is hiring a large number of workers from the company of the member, the member will be encouraged to consider job postings from this company, not only because the chances of landing the job will be higher than average, but also because the member would join former colleagues.

One general aspect includes a method including an operation for identifying, by a server having one or more processors, jobs based on a search for jobs for a member of a social network, where the member works for an employer and each job is associated with a respective company. The server also determines, for each company associated with one or more of the jobs, an intercompany migration score indicating a transition probability that a coworker working for the employer transitions to work for the company. The server further determines, for each job, a job affinity score based on a comparison of data of the job and a profile of the member. The server further ranks the jobs based on the intercompany migration score of the company of the job and the job affinity score, and the server causes presentation of a group including one or more of the ranked jobs in a user interface of the member based on the ranking.

One general aspect includes a system including: a memory including instructions and one or more computer processors. The instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations including identifying jobs based on a search for jobs for a member of a social network, where the member works for an employer and each job is associated with a respective company. The operations further include determining, for each company associated with one or more of the jobs, an intercompany migration score indicating a transition probability that a coworker working for the employer transitions to work for the company. The operations further include determining, for each job, a job affinity score based on a comparison of data of the job and a profile of the member, and ranking the jobs based on the intercompany migration score of the company of the job and the job affinity score. The operations further include causing presentation of a group including one or more of the ranked jobs in a user interface of the member based on the ranking.

One general aspect includes a non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations including identifying jobs based on a search for jobs for a member of a social network, where the member works for an employer and each job is associated with a respective company. The operations further include determining, for each company associated with one or more of the jobs, an intercompany migration score indicating a transition probability that a coworker working for the employer transitions to work for the company. The operations further include determining, for each job, a job affinity score based on a comparison of data of the job and a profile of the member, and ranking the jobs based on the intercompany migration score of the company of the job and the job affinity score. The operations further include causing presentation of a group including one or more of the ranked jobs in a user interface of the member based on the ranking.

FIG. 1 is a block diagram illustrating anetwork architecture102, according to some example embodiments, including asocial networking server112. Thesocial networking server112 provides server-side functionality via a network114 (e.g., the Internet or a wide area network (WAN)) to one ormore client devices104.FIG. 1 illustrates, for example, a web browser106 (e.g., the Internet Explorer® browser developed by Microsoft® Corporation), client application(s)108, and asocial networking client110 executing on aclient device104. Thesocial networking server112 is further communicatively coupled with one ormore database servers126 that provide access to one or more databases116-128.

Theclient device104 may comprise, but is not limited to, a mobile phone, a desktop computer, a laptop, a portable digital assistant (PDA), a smart phone, a tablet, a book reader, a netbook, a multi-processor system, a microprocessor-based or programmable consumer electronic system, or any other communication device that auser130 may utilize to access thesocial networking server112. In some embodiments, theclient device104 may comprise a display module (not shown) to display information (e.g., in the form of user interfaces). In further embodiments, theclient device104 may comprise one or more of touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth.

In one embodiment, thesocial networking server112 is a network-based appliance that responds to initialization requests or search queries from theclient device104. One ormore users130 may be a person, a machine, or another means of interacting with theclient device104. In various embodiments, theuser130 is not part of thenetwork architecture102, but may interact with thenetwork architecture102 via, theclient device104 or another means. For example, one or more portions of thenetwork114 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wi-Fi® network, a WiMax network, another type of network, or a combination of two or more such networks.

Theclient device104 may include one or more applications (also referred to as “apps”) such as, but not limited to, theweb browser106, thesocial networking client110, andother client applications108, such as a messaging application, an electronic mail (email) application, a news application, and the like. In some embodiments, if thesocial networking client110 is present in theclient device104, then thesocial networking client110 is configured to locally provide the user interface for the application and to communicate with thesocial networking server112, on an as-needed basis, for data and/or processing capabilities not locally available (e.g., to access a member profile, to authenticate auser130, to identify or locate other connected members, etc.). Conversely, if thesocial networking client110 is not included in theclient device104, theclient device104 may use theweb browser106 to access thesocial networking server112.

Further, while the client-server-basednetwork architecture102 is described with reference to a client-server architecture, the present subject matter is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example.

In addition to theclient device104, thesocial networking server112 communicates with the one or more database server(s)126 and database(s)116-128. In one example embodiment, thesocial networking server112 is communicatively coupled to amember activity database116, asocial graph database118, amember profile database120, ajobs database122, agroup database128, and acompany database124. Each of the databases116-128 may be implemented as one or more types of database including, but not limited to, a hierarchical database, a relational database, an object-oriented database, one or more flat files, or combinations thereof.

Themember profile database120 stores member profile information about members who have registered with thesocial networking server112. With regard to themember profile database120, the member may include an individual person or an organization, such as a company, a corporation, a nonprofit organization, an educational institution, or other such organizations.

Consistent with some example embodiments, when a user initially registers to become a member of the social networking service provided by thesocial networking server112, the user is prompted to provide some personal information, such as name, age (e.g., birth date), gender, interests, contact information, home town, address, spouse's and/or family members' names, educational background (e.g., schools, majors, matriculation and/or graduation dates, etc.), employment history, professional industry (also referred to herein simply as industry), skills, professional organizations, and so on. This information is stored, for example, in themember profile database120. Similarly, when a representative of an organization initially registers the organization with the social networking service provided by thesocial networking server112, the representative may be prompted to provide certain information about the organization, such as a company industry. This information may be stored, for example, in themember profile database120. In some embodiments, the profile data may be processed (e.g., in the background or offline) to generate various derived profile data. For example, if a member has provided information about various job titles that the member has held with the same company or different companies, and for how long, this information may be used to infer or derive a member profile attribute indicating the member's overall seniority level, or seniority level within a particular company. In some example embodiments, importing or otherwise accessing data from one or more externally hosted data sources may enhance profile data for both members and organizations. For instance, with companies in particular, financial data may be imported from one or more external data sources, and made part of a company's profile.

In some example embodiments, thecompany database124 stores information regarding companies in the member's profile. A company may also be a member, but some companies may not be members of the social network although some of the employees of the company may be members of the social network. Thecompany database124 includes company information, such as name, industry, contact information, website, address, location, geographic scope, and the like.

As members interact with the social networking service provided by thesocial networking server112, thesocial networking server112 is configured to monitor these interactions. Examples of interactions include, but are not limited to, commenting on posts entered by other members, viewing member profiles, editing or viewing a member's own profile, sharing content from outside of the social networking service (e.g., an article provided by an entity other than the social networking server112), updating a current status, posting content for other members to view and comment on, job suggestions for the members, job-post searches, and other such interactions. In one embodiment, records of these interactions are stored in themember activity database116, which associates interactions made by a member with his or her member profile stored in themember profile database120. In one example embodiment, themember activity database116 includes the posts created by the members of the social networking service for presentation on member feeds.

Thejobs database122 includes job postings offered by companies in thecompany database124. Each job posting includes job-related information such as any combination of employer, job title, job description, requirements for the job, salary and benefits, geographic location, one or more job skills required, date the job was posted, relocation benefits, and the like.

Thegroup database128 includes group-related information. As used herein, a group includes jobs that are selected based on a group characteristic that provides an indication of why the jobs in the group are selected for presentation to the member. Examples of group characteristics include inter-company migrations of workers, relationships between an educational institution of the member and the employees of a company who also attended the educational institution, virtual teams in the company with profiles similar to the member's profile, cultural fit of the member within the company, social connections of the member who work at the company, and the like.

Members of the social networking service may establish connections with one or more members of the social networking service. The connections may be defined as a social graph, where the member is represented by a vertex in the social graph and the edges identify connections between vertices. Members are said to be first-degree connections where a single edge connects the vertices representing the members; otherwise, members are said to have connections of the n^thdegree, where n is defined as the number of edges separating two vertices. In one embodiment, the social graph maintained by thesocial networking server112 is stored in thesocial graph database118.

In one embodiment, thesocial networking server112 communicates with the various databases116-128 through the one or more database server(s)126. In this regard, the database server(s)126 provide one or more interfaces and/or services for providing content to, modifying content in, removing content from, or otherwise interacting with the databases116-128. For example, and without limitation, such interfaces and/or services may include one or more Application Programming Interfaces (APIs), one or more services provided via a Service-Oriented Architecture (SOA), one or more services provided via a REST-Oriented Architecture (ROA), or combinations thereof. In an alternative embodiment, thesocial networking server112 communicates directly with the databases116-128 and includes a database client, engine, and/or module, for providing data to, modifying data stored within, and/or retrieving data from the one or more databases116-128.

While the database server(s)126 are illustrated as a single block, one of ordinary skill in the art will recognize that the database server(s)126 may include one or more such servers. For example, the database server(s)126 may include, but are not limited to, a Microsoft® Exchange Server, a Microsoft® Sharepoint® Server, a Lightweight Directory Access Protocol (LDAP) server, a MySQL database server, or any other server configured to provide access to one or more of the databases116-128, or combinations thereof. Accordingly, and in one embodiment, the database server(s)126 implemented by the social networking service are further configured to communicate with thesocial networking server112.

FIG. 2 is a screenshot of auser interface200 that includes recommendations for jobs202-206, according to some example embodiments. In one example embodiment, the social network user interface provides job recommendations, which are job postings that match the job interests of the user and that are presented without a specific job search request from the user (e.g., job suggestions).

In another example embodiment, a job search interface is provided for entering job searches, and the resulting job matches are presented to the user in theuser interface200.

As the user scrolls down theuser interface200, more job recommendations are presented to the user. In some example embodiments, the job recommendations are prioritized to present jobs in an estimated order of interest to the user.

Theuser interface200 presents a “flat” list of job recommendations as a single list. Other embodiments presented below utilize a “segmented” list of job recommendations where each segment is a group that is associated with a related reason indicating why these jobs are being recommended within the group.

FIG. 3 is a screenshot of a user's profile view, according to some example embodiments. Each user in the social network has amember profile302, which includes information about the user. Themember profile302 is configurable by the user and also includes information based on the user's activity in the social network (e.g., likes, posts read).

In one example embodiment, themember profile302 may include information in several categories, such as aprofile picture304,experience308,education310, skills andendorsements312,accomplishments314,contact information334, following316, and the like. Skills include professional competences that the member has, and the skills may be added by the member or by other members of the social network. Example skills include C++, Java, Object Programming, Data Mining, Machine Learning, Data Scientist, and the like. Other members of the social network may endorse one or more of the skills and, in some example embodiments, the member's account is associated with the number of endorsements received for each skill from other members.

Theexperience308 information includes information related to the professional experience of the user. In one example embodiment, theexperience308 information includes anindustry306, which identifies the industry in which the user works. In one example embodiment, the user is given an option to select an industry from a plurality of industries when entering this value in themember profile302. Theexperience308 information area may also include information about the current job held by the user and previous jobs held by the user. By analyzing the job history of a user, the intercompany migrations may be determined. As used herein, an intercompany migration for a user takes place when the user moves from one company to another (e.g., the user changes the company where the user is working).

Theeducation310 information includes information about the educational background of the user, including the educational institutions attended by the user, the degrees obtained, and the field of study of the degrees. For example, a member may list that the member attended the University of Michigan and obtained a graduate degree in computer science.

The skills andendorsements312 information includes information about professional skills that the user has identified as having been acquired by the user and endorsements entered by other users of the social network supporting the skills of the user. Theaccomplishments314 area includes accomplishments entered by the user, and thecontact information334 includes contact information for the user, such as an email address and phone number. The following316 area includes the names of entities in the social network being followed by the user.

FIG. 4 is a diagram of auser interface402, according to some example embodiments, for presenting job postings to a member of the social network. Theuser interface402 includes theprofile picture304 of the member, asearch section404, adaily jobs section406, and one ormore group areas408. In some example embodiments, a message next to theprofile picture304 indicates the goal of the search, e.g., “Looking for a senior designer position in New York City at a large Internet company.”

Thesearch section404, in some example embodiments, includes two boxes for entering search parameters: a keyword input box for entering any type of keywords for the search (e.g., job title, company name, job description, skill, etc.), and a geographic area input box for entering a geographic area for the search (e.g., New York). This allows members to execute searches based on keyword and location. In some embodiments, the geographic area input box includes one or more of city, state, ZIP Code, or any combination thereof.

In some example embodiments, the search boxes may be pre filled with the user's title and location if no search has been entered yet. Clicking the search button causes the search of jobs based on the keyword inputs and location. It is to be noted that the inputs are optional, and only one search input may be entered at a three or both search boxes may be filled in.

Thedaily jobs section406 includes information about one or more jobs selected for the user, based on one or more parameters, such as member profile data, search history, job match to the member, recentness of the job, whether the user is following the job, and so forth.

Eachgroup area408 includes one ormore jobs202 for presentation in theuser interface402. In one example embodiment, thegroup area408 includes one to six jobs with an option to scroll thegroup area408 to present additional jobs, if available.

Eachgroup area408 provides an indication of why the r being presented with those jobs, which identifies the characteristic of the group. There could be several types of reasons related to the connection of the user to the job, the affinity of the member to the group, the desirability of the job, or the time deadline of the job (e.g, urgency). The reasons related to the connection of the user to the job may include relationships between the job and the social connections of the member (e.g., “Your connections can refer you to this set of jobs”), a quality of a fit between the job and the user characteristics (e.g., “This is a job from a company that hires from your school”), a quality of a match between the member's talent and the job (e.g., “You would be in the top 90% of all applicants), the number or frequency, of employees going from a given company to another company, and so forth.

Further, the group characteristics may be implicit (e.g., “These jobs are recommended based on your browsing history”) or explicit (e.g., “These are jobs from companies you followed”). The desirability reasons may include popularity of the job in the member's area (e.g., most-viewed by other members or most applications received), jobs from in-demand start-ups in the member's area, and popularity of the job among people with the same title as the member. Further yet, the time-urgency reasons may include “Be the first to apply to these jobs,” or “These jobs will be expiring soon.”

It is to be noted that the embodiments illustrated inFIG. 4 are examples and do not describe every possible embodiment. Other embodiments may utilize different layouts or groups, present fewer or more jobs, present fewer or more groups, and so forth. The embodiments illustrated inFIG. 4 should therefore not be interpreted to be exclusive or limiting, but rather illustrative.

FIG. 5 is a detail of an intercompany-migration group area408 in the user interface ofFIG. 4, according to some example embodiments. In one example embodiment, thegroup area408 is for a group referred to as an intercompany migration group, which provides an indication of how other members of the social network have “transitioned” from the same company where the member is working to other companies where coworkers obtained employment. An intercompany migration takes place when a member changes jobs and changes from working at a first company (the source company) to working at a second company (the destination company). In some example embodiments, the direct intercompany migrations are utilized, i.e., when a member goes directly from one company to another, but in other example embodiments, indirect intercompany migrations are also utilized, where an indirect intercompany migration takes place when a member goes to work at the destination company after working in an intermediate company, or companies, before making the migration.

In one example embodiment, the intercompany-migration group area408 includes profile pictures502 of people who migrated from the same company where the user is working (also referred to herein as the “employer” or the “employer company”) to other companies. Other example embodiments may include names instead of, or in addition to, the profile pictures502. If a profile picture is not available for a user, a “ghost” picture may be displayed, where a ghost picture is a generic icon for a user without a profile picture. In addition, the intercompany-migration group area408 includesicons504 of some of the companies where coworkers have migrated to, and a plurality ofjobs202 relevant to this group. It is noted that, in general, “coworkers” refers to members of the social network that are currently working for the current employer of the member or that previously worked for the current employer. If additional jobs related to the group are available for presentation, scroll selectors are available to view the additional jobs. Often, when discussing intercompany migrations, the term “coworker” refers to former employees of the employer company, because if the coworker still works for the employer, then there is no intercompany migration from the employer, unless the coworker returned to the employer company.

Eachjob202 includes information about the job and information about the coworkers that joined the company offering the job. In some example embodiments, thejob202 description includes the job title, logo and name of the company, job location, and job statistics, such as the number of days since the job was first posted, the number of members who have viewed the job, and the number of applications for the job received in the social network. In addition, any combination of profile pictures, member names, and member titles may be included to identify the connections of the member to the job via202 the member's colleagues.

FIG. 6 illustrates data structures for storing job and member information, according to some example embodiments. Themember profile302, as discussed above, includes member information, such as name, title (e.g., job title), industry (e.g., legal services), geographic region, employer, skills and endorsements, and so forth. In some example embodiments, themember profile302 also includes job-related data, such as jobs previously applied to, or jobs already suggested to the member (and how many times each job has been suggested to the member). Within themember profile302, the skill information is linked toskill data602, and the employer information is linked tocompany data606.

In one example embodiment, thecompany data606 includes company information, such as company name, industry associated with the company, number of employees at the company, address of the company, overview description of the company, job postings associated with the company, and the like

Theskill data602 is a table for storing the different skills identified in the social network. In one example embodiment, theskill data602 includes a skill identifier (ID) (e.g., a numerical value or a text string) and a name for the skill. The skill identifier may be linked to themember profile302 andjob202 data.

In one example embodiment, thejob202 data includes data for jobs posted by companies in the social network. Thejob202 data includes one or more of a title associated with the job (e.g., Software Developer), a company that posted the job, a geographic region where the job is located, a description of the job, a type of the job, qualifications required for the job, and one or more skills. Thejob202 data may be linked to thecompany data606 and theskill data602.

It is to be noted that the embodiments illustrated inFIG. 6 are examples and do not describe every possible embodiment. Other embodiments may utilize different data structures or fewer data structures, combine the information from two data structures into one, have additional or fewer links among the data structures, and the like. The embodiments illustrated inFIG. 6 should therefore not be interpreted to be exclusive or limiting, but rather illustrative.

FIGS. 7A-7B illustrate the scoring of a job for a member, according to some example embodiments.FIG. 7A illustrates the scoring, also referred to herein as ranking, of ajob202 for a member associated with amember profile302 based on ajob affinity score706.

Thejob affinity score706, between a job and a member, is a value that measures how well the job matches the interest of the member in finding the job. A so called “dream job” for a member would be the perfect job for the member and would have a high, or even maximum, value, while a job that the member is not interested in at all (e.g., in a different professional industry) would have a lowjob affinity score706. In some example embodiments, thejob affinity score706 is a value between zero and one, or a value between zero and 100, although other ranges are possible.

In some example embodiments, a machine-learning program is used to calculate the job affinity scores for the jobs available to the member. The machine-learning program is trained with existing data in the social network, and the machine-learning program is then used to evaluate jobs based on the features used by the machine-learning program in some example embodiments, the features include any combination of job data (e.g., job title, job description, company, geographic location, etc.), member profile data, member search history, employment of social connections of the member, job popularity in the social network, number of days the job has been posted, company reputation, company size, company age, profit vs. nonprofit company, and pay scale. More details are provided below with reference toFIG. 8 regarding the training and use of the machine-learning program.

FIG. 7B illustrates the scoring of ajob202 for a member associated with themember profile302, according to some example embodiments, based on three parameters: thejob affinity score706, a job-to-group score708, and agroup affinity score710. Broadly speaking, thejob affinity score706 indicates how relevant thejob202 is to the member, the job-to-group score708 indicates how relevant thejob202 is to agroup712, and thegroup affinity score710 indicates how relevant thegroup712 is to the member.

Thegroup affinity score710 indicates how relevant thegroup712 is to the member, where a high affinity score indicates that thegroup712 is very relevant to the member and should be presented in the user interface, while a low affinity score indicates that thegroup712 is not relevant to the member and may be omitted from presentation in the user interface.

Thegroup affinity score710 is used, in some example embodiments, to determine whichgroups712 are presented in the user interface, as discussed above, and thegroup affinity score710 is also used to order thegroups712 when presenting them in the user interface, such that thegroups712 may be presented in the order of their respective group affinity scores710. It is to be noted that if there is not enough “liquidity” of jobs for a group712 (e.g., there are not enough jobs for presentation in the group712), thegroup712 may be omitted from the user interface or presented with lower priority, even if thegroup affinity score710 is high.

In some example embodiments, a machine-learning program is utilized for calculating thegroup affinity score710. The machine-learning program is trained with member data, including interactions of users with thedifferent groups712. The data for the particular member is then utilized by the machine-learning program to determine thegroup affinity score710 for the member with respect to aparticular group712. The features utilized by the machine-learning program include the history of interaction of the member with jobs from thegroup712, click data for the member (e.g., a click rate based on how many times the member has interacted with the group712), member interactions with other members who have a relationship to thegroup712, and the like. For example, one feature may include an attribute that indicates if the member is a student, and if the member is a student, features such as social connections or education-related attributes will be important to determine which groups are of interest to the student. On the other hand, a member who has been out of school for 20 years or more may not be as interested in education-related features.

Another feature of interest to determine group participation is whether the member has worked in small companies or large companies throughout a long career. If the member exhibits a pattern of working for large companies, a group that provides jobs for large companies would likely be of more interest to the member than a group that provides jobs in small companies, unless there are other factors, such as recent interaction of the member with jobs from small companies.

The job-to-group score708 between ajob202 and agroup712 indicates thejob202's strength within the context of thegroup712, where a high job-to-group score708 indicates that thejob202 is a good candidate for presentation within thegroup712 and a low job-to-group score708 indicates that thejob202 is not a good candidate for presentation within thegroup712. In some example embodiments, a predetermined threshold is identified, whereinjobs202 with a job-to-group score708 equal to or above the predetermined threshold are included in thegroup712 andjobs202 with a job-to-group score708 below the predetermined threshold are not included in thegroup712.

For example, in agroup712 that presents jobs for the intercompany-migration group, if there is ajob202 for a company, the job-to-group score708 indicates how often coworkers are migrating to get jobs in the company of thejob202. In another example, in a group within the social network of the member, if there is ajob202 for a company within the network of the member, the job-to-group score708 indicates how strong the member's network is for reaching the company of thejob202.

In some example embodiments, thejob affinity score706, the job-to-group score708, and thegroup affinity score710 are combined to obtain a combined score714 for thejob202. The scores may be combined utilizing addition, weighted averaging, or other mathematical operations.

FIG. 7B illustrates that, for a givenjob202 andmember profile302, there may be a plurality ofgroups712 G1, . . . , GN. Embodiments presented herein identify which jobs fit better in which group, and which groups have higher priority for presentation to the member.

In the intercompany-migration group, the job-to-group score708 measures how many coworkers of the member made the transition from the employer to the company associated with the job posting. It provides an indication of whether the company is hiring relatively few or many people working for the employer of the member. This is useful, because if the company is hiring relatively many coworkers, then the member has a better chance of landing the job with the company. Also, the member may benefit from working with former colleagues, and the member may have connections that may help land the job.

FIG. 8 illustrates the training and use of a machine-learning program816, according to some example embodiments. In some example embodiments, machine-learning programs, also referred to as machine-learning algorithms or tools, are utilized to perform operations associated with job searches.

Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Machine learning explores the study and construction of algorithms, also referred to herein as tools, that may learn from existing data and make predictions about new data. Such machine-learning tools operate by building a model fromexample training data812 in order to make data-driven predictions or decisions expressed as outputs orassessments820. Although example embodiments are presented with respect to a few machine-learning tools, the principles presented herein may be applied to other machine-learning tools.

In some example embodiments, different machine-learning tools may be used. For example, Logistic Regression (LR), Naive-Bayes, Random Forest (RF), neural networks (NN), matrix factorization, and Support Vector Machines (SVM) tools may be used for classifying or scoring job postings.

In general, there are two types of problems in machine learning: classification problems and regression problems. Classification problems aim at classifying items into one of several categories (for example, is this object an apple or an orange?). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number). In some embodiments, example machine-learning algorithms provide a job affinity score706 (e.g., a number from1 to100) to qualify each job as a match for the user (e.g., calculating the job affinity score). In other example embodiments, machine learning is also utilized to calculate thegroup affinity score710 and the job-to-group score708. The machine-learning algorithms utilize thetraining data812 to find correlations among identifiedfeatures802 that affect the outcome.

In one example embodiment, thefeatures802 may be of different types and may include one or more of member features804, job features806, company features808, andother features810. The member features804 may include one or more of the data in themember profile302, as described inFIG. 6, such as title, skills, experience, education, and the like. The job features806 may include any data related to thejob202, and the company features808 may include any data related to the company. In some example embodiments, additional features in theother features810 may be included, such as post data, message data, web data, and the like.

With thetraining data812 and the identified features802, the machine-learning tool is trained atoperation814. The machine-learning tool appraises the value of thefeatures802 as they correlate to thetraining data812. The result of the training is the trained machine-learning program816.

When the machine-learning program816 is used to perform an assessment,new data818 is provided as an input to the trained machine-learning program816, and the machine-learning program816 generates theassessment820 as output. For example, when a member performs a job search, a machine-learning program, trained with social network data, uses the member data and job data from the jobs in the database to search for jobs that match the member's profile and activity.

FIG. 9 illustrates the transitions of members between companies, according to some example embodiments. The intercompany migration group is related to the transitions, ofcoworkers906 of theuser130, from anemployer910 to other companies908 C₁-C_M. The goal is to findcompanies908 that are hiring people from the member'semployer910.

In some example embodiments, all thecoworkers906 of theemployer910 are considered for the analysis, while in other embodiments, the analysis takes into consideration only thecoworkers906 with the same job function or job title as the job. In other example embodiments, only thecoworkers906 with the same skill set as theuser130 are utilized.

The analysis for the intercompany migration group searches forcoworker906 transitions to identify thecompanies908 where thecoworkers906 found jobs. For example, some companies tend to hire from competitors. If theuser130 wants to go to a competitor, then theuser130 is probably interested in looking at jobs at competitors that are hiring a large number ofcoworkers906.

In some example embodiments, two transition probabilities are calculated, referred to as anoutbound probability Poutbound902 and aninbound probability Pinbound904. Theoutbound probability Poutbound902 measures the probability that a member goes from theemployer910 to acompany908, and theinbound probability Pinbound904 measures the probability that a worker of a company comes from the employer910 (e.g., worked previously for the employer).

The social network has information regarding the intercompany migrations because most members of the social network include their job history in their profiles. Therefore, in one example embodiment, the probabilities are calculated utilizing member profile data from the social network.

Theoutbound probability Poutbound902 between theemployer910 and acompany908 is calculated as the number of people from theemployer910 who joined thecompany908 divided by the number of people from theemployer910 who joined thecompany908 as well as other companies, that is, the fraction of people that joined thecompany908 after quitting from theemployer910 compared to everyone that quit from theemployer910. In some embodiments, time limits may be utilized for the probability calculations, such as by taking into account onlycoworkers906 that worked for theemployer910 within the last year, or the last 5 years, or some other identified period.

The outbound probability is good for comparing large companies. In other example embodiments, the probabilities are based on weighted values related to the amount of time elapsed since a member joined the company, where recent transitions have higher weights than older transitions.

If the outbound probability were utilized exclusively, then small companies would be missed, because their probabilities would be much smaller than those of large companies. Therefore, the inbound probability is used to include jobs from small companies because the inbound probability provides the perspective from the company, not from the employer.

Theinbound probability Pinbound904 is calculated as the number of people from theemployer910 who joined thecompany908 divided by the number of people from theemployer910 as well as other companies who joined thecompany908, that is, the fraction of people that joined thecompany908 after quitting from theemployer910 compared to everyone that joined thecompany908. Therefore, the difference between the inbound probability and the outbound probability is the denominator used in the calculation.

For example, if there is a 10-person startup formed by five former employees of the employer, the probability that the startup will hire m the employer is high because the inbound probability is 0.5. However, since the startup is very small, the outbound probability would be very small if the employer has a large number of employees.

FIG. 10 illustrates a method for selecting jobs for presentation within a group, according to some example embodiments. Theoutbound probabilities Poutbound902 are calculated for each pair of employer E and company C_j, represented as Poutbound(E, C_j). In addition, theinbound probabilities Pinbound904 are calculated for each pair, represented as Pinbound(E, C_j).

An intercompany migration score β1002 is calculated based on theoutbound probability Poutbound902 and theinbound probability Pinbound904. The intercompany migration score between the employer E and a company C_jis referred to as β(E, C_j), and is calculated utilizing a function G based oar Poutbound(E, C_j) and Pinbound(E, C_j). In one example embodiment, the G function is the average of Poutbound(E, C_j) and Pinbound(E, C_j), but in other embodiments, other functions may be utilized, such as the sum, a weighted average of the two values, the harmonic mean of the two values, the geometric mean of the two values and the like.

The intercompany migration score β1002 is the job-to-group score708 for the intercompany-migration group. It is to be noted that some values may be pre-calculated before a job search is performed for a user. For example, the intercompany migration scores may be calculated for, at least, the most common pairs of companies. Further, thegroup affinity score710 for a given member may be pre-calculated also.

Atoperation1006, a job search is performed for member M. The job search may be originated by the member or may be originated by the social network in order to propose job postings to the member. Theresult1008 is a plurality of job candidates J_jfor presentation to the member based on their affinity scores S(M, J_j).

Atoperation1010, which is optional in some embodiments, the candidate jobs may be filtered. In one example embodiment, the candidate jobs having a job-to-group score higher than a predetermined threshold are considered. In this case, the jobs from companies with an intercompany migration score β1002 greater than the predetermined threshold are considered. In other example embodiments, all the candidate jobs are considered and filtering is not performed.

Atoperation1012, a member-job-company score γ_j(M, J_j, C_j) is calculated for each job J_jby combining the intercompany migration score β(E, C_j), where C_jis the company posting job J_j, and the job affinity score S(M, J_j). The combination may be performed by multiplying the scores, by adding the scores, by performing a weighted multiplication, by performing a weighted addition, by calculating the geometric mean or the average, and so forth.

The candidate jobs are then ranked according to their member-job-company score γ_j, where the best jobs for the member M will be at the top of the ranked list of candidate jobs. In some example embodiments, the machine-learning program is used to rank the jobs based on their β and S scores. The machine-learning program is trained with activity data of members of the social network, and then the member activity and the different job-related scores are used to rank the jobs for the member.

At operation1014, a predetermined number of the top job candidates is selected for presentation in the group area of the user interface. For example, six jobs may be presented per group (as long as there are six jobs available for each group), or a different number of jobs may be presented per group, such as a number in the range from one to ten. Further, in some example embodiments, groups with higher rankings may present more jobs than groups with lower rankings. For example, a top group may present ten jobs, and each of the remaining groups may present four jobs.

Atoperation1016, the selected jobs are presented in the user interface. It is to be noted that the different groups are ranked according to their scores and then placed in the order of their ranking in the user interface.

FIG. 11 illustrates asocial networking server112 for implementing example embodiments. In one example embodiment, thesocial networking server112 includes asearch server1102, auser interface module1104, a job search/suggestions engine1106, a jobgroup coordinator server1108, a jobaffinity scoring server1110, a job-to-group scoring server1112, a groupaffinity scoring server1114, and a plurality of databases, which include thesocial graph database118, themember profile database120, thejobs database122, themember activity database116, thegroup database128, and thecompany database124.

Thesearch server1102 performs data searches on the social network, such as searches for members or companies. In some example embodiments, thesearch server1102 includes a machine-learning algorithm for performing the searches, which utilizes a plurality of features for selecting and scoring the jobs. The features include, at least, one or more of title, industry, skills, member profile, company profile, job title, job data, region, and salary range. Theuser interface module1104 communicates with theclient devices104 to exchange user interface data for presenting the user interface to the user. The job search/suggestions engine1106 performs job searches based on a search query (e.g., using one or more keywords and a geographic location as illustrated inFIG. 4) or based on a member profile in order to offer job suggestions.

The jobaffinity scoring server1110 calculates the job affinity scores, as illustrated above with reference toFIGS. 7A-7B and 8-10. The job-to-group scoring server1112 calculates the job-to-group scores, as illustrated above with reference toFIGS. 7B and 8-10. The groupaffinity scoring server1114 calculates the group affinity scores, as illustrated above with reference toFIGS. 7B and 8-10.

The jobgroup coordinator server1108 calculates the combined score for the scores identified above. The jobgroup coordinator server1108 further ranks the different groups in order to determine the priority of presentation of the groups in the user interface and which groups will be presented or omitted. In addition, the jobgroup coordinator server1108 may determine in which group to present a job, if the job could be presented in two or more groups.

It is to be noted that the embodiments illustrated inFIG. 11 are examples and d not describe every possible embodiment. Other embodiments may utilize different servers or additional servers, combine the functionality of two or more servers into a single server, utilize a distributed server pool, and so forth. The embodiments illustrated inFIG. 11 should therefore not be interpreted to be exclusive or limiting, but rather illustrative.

FIG. 12 is a flowchart of amethod1200, according to some example embodiments, for searching job postings for a member of a social network based on the transitions of members between companies. While the various operations in this flowchart are presented and described sequentially, one of ordinary skill will appreciate that some or all of the operations may be executed a different order, be combined or omitted, or be executed in parallel.Operation1202 is for identifying, by a server having one or more processors, jobs based on a search for jobs for a member of a social network, where the member works for an employer and each job is associated with a respective company.

Fromoperation1202, themethod1200 flows tooperation1204 where the server determines, for each company associated with one or more of the jobs, an intercompany migration score indicating a transition probability that a coworker working for the employer transitions to work for the company.

Further, atoperation1206, the server determines, for each job, a job affinity score based on a comparison of data of the job and a profile of the member. Fromoperation1206, themethod1200 flows tooperation1208, where the server ranks the jobs based on the intercompany migration score of the company of the job and the job affinity score.

Fromoperation1208, themethod1200 flows tooperation1210, where the server causes presentation of a group including one or more of the ranked jobs in a user interface of the member based on the ranking.

In one example, themethod1200 calculates an outbound probability of a transition from the employer to a company as a number of coworkers that transitioned from the employer to the company divided by a number of employees in the employer. Further, themethod1200 calculates an inbound probability of a transition from the employer to a company as a number of coworkers that transitioned from the employer to the company divided by a number of employees in the company. Further yet, determining the intercompany migration score between the employer and the company is based on the inbound probability and the outbound probability.

In another example, determining the job affinity score is performed by a machine-learning program based on the data of the job and the profile of the member, with the machine-learning program being trained utilizing data of job postings in the social network and data of members of the social network.

In another example, transitions from the employer to the company are assigned weights based on a time when an employee joined the company, where recent transitions are given higher weights for calculating the intercompany migration score than older transitions.

In some example embodiments, the user interface for presentation of the group further includes indications of coworkers that migrated to other companies.

In other examples, the user interface for presentation of the group presents a predetermined number of jobs with an option for scrolling to see additional jobs.

In one example, the user interface further presents additional groups, where the groups are sorted based on respective job affinity scores of jobs within each group, group affinity scores for each group, and job-to-group scores for each group.

In some examples, themethod1200 further includes calculating a group affinity score for the member based on interactions of the member related to job searches or job applications for a plurality of companies.

FIG. 13 is a block diagram1300 illustrating arepresentative software architecture1302, which may be used in conjunction with various hardware architectures herein described.FIG. 13 is nerdy a non-limiting example of asoftware architecture1302, and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. Thesoftware architecture1302 may be executing on hardware such as amachine1400 ofFIG. 14 that includes, among other things,processors1404, memory/storage1406, and input/output (I/O)components1418. Arepresentative hardware layer1350 is illustrated and can represent, for example, themachine1400 ofFIG. 14. Therepresentative hardware layer1350 comprises one ormore processing units1352 having associatedexecutable instructions1354. Theexecutable instructions1354 represent the executable instructions of thesoftware architecture1302, including implementation of the methods, modules, and so forth ofFIGS. 1-6, 8, and 10-12. Thehardware layer1350 also includes memory and/orstorage modules1356, which also have theexecutable instructions1354. Thehardware layer1350 may also compriseother hardware1358, which represents any other hardware of thehardware layer1350, such as the other hardware illustrated as part of themachine1400.

In the example architecture ofFIG. 13, thesoftware architecture1302 may be conceptualized as a stack of layers where each layer provides particular functionality. For example, thesoftware architecture1302 may include layers such as anoperating system1320,libraries1316, frameworks/middleware1314,applications1312, and apresentation layer1310. Operationally, theapplications1312 and/or other components within the layers may invoke API calls1304 through the software stack and receive a response, returned values, and so forth illustrated asmessages1308 in response to the API calls1304. The layers illustrated are representative in nature, and not all software architectures have all layers. For example, some mobile or special-purpose operating systems may not provide a frameworks/middleware layer1314, while others may provide such a layer. Other software architectures may include additional or different layers.

Theoperating system1320 may manage hardware resources and provide common services. Theoperating system1320 may include, for example, akernel1318,services1322, anddrivers1324. Thekernel1318 may act as an abstraction layer between the hardware and the other software layers. For example, thekernel1318 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. Theservices1322 may provide other common services for the other software layers. Thedrivers1324 may be responsible for controlling or interfacing with the underlying hardware. For instance, thedrivers1324 may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration.

Thelibraries1316 may provide a common infrastructure that may be utilized by theapplications1312 and/or other components and/or layers. Thelibraries1316 typically provide functionality that allows other software modules to perform tasks in an easier fashion than by interfacing directly with theunderlying operating system1320 functionality (e.g.,kernel1318,services1322, and/or drivers1324). Thelibraries1316 may include system libraries1342 (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, thelibraries1316 may includeAPI libraries1344 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render two-dimensional and three-dimensional graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. Thelibraries1316 may also include a wide variety ofother libraries1346 to provide many other APIs to theapplications1312 and other software components/modules.

The frameworks1314 (also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by theapplications1312 and/or other software components/modules. For example, theframeworks1314 may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. Theframeworks1314 may provide a broad spectrum of other APIs that may be utilized by theapplications1312 and/or other software components/modules, some of which may be specific to a particular operating system or platform.

Theapplications1312 include job-scoringapplications1362 job search/suggestions1364, built-inapplications1336, and third-party applications1338. The job-scoringapplications1362 comprise the job-scoring applications, as discussed above with reference toFIG. 11. Examples of representative built-inapplications1336 may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. The third-party applications1338 may include any of the built-inapplications1336 as well as a broad assortment of other applications. In a specific example, the third-party application1338 (e.g., an application developed using the Android™ or iOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as iOS™, Android™, Windows® Phone, or other mobile operating systems. In this example, the third-party application1338 may invoke the API calls1304 provided by the mobile operating system such as theoperating system1320 to facilitate functionality described herein.

Theapplications1312 may utilize built-in operating system functions (e.g.,kernel1318,services1322, and/or drivers1324), libraries (e.g.,system libraries1342,API libraries1344, and other libraries1346), or frameworks/middleware1314 to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as thepresentation layer1310. In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 13, this is illustrated by avirtual machine1306. A virtual machine creates a software environment where applications/modules can execute as if they were executing on a hardware machine (such as themachine1400 ofFIG. 14, for example). Thevirtual machine1306 is hosted by a host operating system (e.g.,operating system1320 inFIG. 13) and typically, although not always, has avirtual machine monitor1360, which manages the operation of thevirtual machine1306 as well as the interface with the host operating system (e.g., operating system1320). A software architecture executes within thevirtual machine1306, such as anoperating system1334,libraries1332, frameworks/middleware1330,applications1328, and/or apresentation layer1326. These layers of software architecture executing within thevirtual machine1306 can be the same as corresponding layers previously described or may be different.

FIG. 14 is a block diagram illustrating components of amachine1400, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,FIG. 14 shows a diagrammatic representation of themachine1400 in the example form of a computer system, within which instructions1410 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing themachine1400 to perform any one or more of the methodologies discussed herein may be executed. For example, theinstructions1410 may cause themachine1400 to execute the flow diagrams ofFIGS. 10 and 12. Additionally, or alternatively, theinstructions1410 may implement the job-scoring programs and the machine-learning programs associated with them. Theinstructions1410 transform the general,non-programmed machine1400 into aparticular machine1400 programmed to carry out the described and illustrated functions in the manner described.

In alternative embodiments, themachine1400 operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, themachine1400 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. Themachine1400 may comprise, but not be limited to, a switch, a controller, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing theinstructions1410, sequentially or otherwise, that specify actions to be taken by themachine1400. Further, while only asingle machine1400 is illustrated, the term “machine” shall also be taken to include a collection ofmachines1400 that individually or jointly execute theinstructions1410 to perform any one or more of the methodologies discussed herein.

Themachine1400 may includeprocessors1404, memory/storage1406, and I/O components1418, which may be configured to communicate with each other such as via abus1402. In an example embodiment, the processors1404 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (AMC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor1408 and aprocessor1412 that may execute theinstructions1410. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. AlthoughFIG. 14 showsmultiple processors1404, themachine1400 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory/storage1406 may include amemory1414, such as a main memory, or other memory storage, and astorage unit1416, both accessible to theprocessors1404 such as via thebus1402. Thestorage unit1416 andmemory1414 store theinstructions1410 embodying any one or more of the methodologies or functions described herein. Theinstructions1410 may also reside, completely or partially, within thememory1414, within thestorage unit1416, within at least one of the processors1404 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by themachine1400. Accordingly, thememory1414, thestorage unit1416, and the memory of theprocessors1404 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)), and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media e.g., a centralized or distributed database, or associated caches and servers) able to store theinstructions1410. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions1410) for execution by a machine (e.g., machine1400), such that the instructions, when executed by one or more processors of the machine (e.g., processors1404), cause the machine to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se.

The I/O components1418 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components1418 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components1418 may include many other components that are not shown inFIG. 14. The I/O components1418 are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components1418 may includeoutput components1426 andinput components1428. Theoutput components1426 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. Theinput components1428 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components1418 may includecommunication components1440 operable to couple themachine1400 to anetwork1432 ordevices1420 via acoupling1424 and acoupling1422, respectively. For example, thecommunication components1440 may include a network interface component or other suitable device to interface with thenetwork1432. In further examples, thecommunication components1440 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. Thedevices1420 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

Moreover, thecommunication components1440 may detect identifiers or include components operable to detect identifiers. For example, thecommunication components1440 may include Radio Frequency Identification (RFID) tag reader components, NEC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via thecommunication components1440, such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

In various example embodiments, one or more portions of thenetwork1432 may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, thenetwork1432 or a portion of thenetwork1432 may include a wireless or cellular network and thecoupling1424 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, thecoupling1424 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EN/DO) technology, General Packet Radio Service (CPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined h various standard-setting organizations, other long range protocols, or other data transfer technology.

Theinstructions1410 may be transmitted or received over thenetwork1432 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components1440) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, theinstructions1410 may be transmitted or received using a transmission medium via the coupling1422 (e.g., a peer-to-peer coupling) to thedevices1420. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying theinstructions1410 for execution by themachine1400, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

Claims

What is claimed is:

1. A method comprising:

identifying, by a server having one or more processors, jobs based on a search for jobs for a member of a social network, the member working for an employer, each job being associated with a respective company;

for each company associated with one or more of the jobs, determining, by the server, an intercompany migration score indicating a transition probability that a coworker working for the employer transitions to work for the company;

for each job, determining, by the server, a job affinity score based on a comparison of data of the job and a profile of the member;

ranking, by the server, the jobs based on the intercompany migration score of the company of the job and the job affinity score; and

causing, by the server, presentation of a group including one or more of the ranked jobs in a user interface of the member based on the ranking.

2. The method as recited inclaim 1, further comprising:

calculating an outbound probability of a transition from the employer to a company as a number of coworkers that transitioned from the employer to the company divided by a number of coworkers that transitioned from the employer to all companies.

3. The method as recited inclaim 2, further comprising:

calculating an inbound probability of a transition from the employer to a company as a number of coworkers that transitioned from the employer to the company divided by a number of people that transitioned from all companies to the company.

4. The method as recited inclaim 3, wherein determining the intercompany migration score between the employer and the company is based on the inbound probability and the outbound probability.

5. The method as recited inclaim 1, wherein determining the job affinity score is performed by a machine-learning program based on the data of the job and the profile of the member, the machine-learning program being trained utilizing data of job postings in the social network and data of members of the social network.

6. The method as recited inclaim 1, wherein transitions from the employer to the company are assigned weights based on a time when an employee joined the company, wherein recent transitions are given higher weights for calculating the intercompany migration score than older transitions.

7. The method as recited inclaim 1, wherein the user interface for presentation of the group further comprises indications of coworkers that migrated to other companies.

8. The method as recited inclaim 1, wherein the user interface for presentation of the group presents a predetermined number of jobs with an option for scrolling to see additional jobs.

9. The method as recited inclaim 1, wherein the user interface further presents additional groups, wherein the groups are sorted based on respective job affinity scores of jobs within each group, group affinity scores for each group, and job-to-group scores for each group.

10. The method as recited inclaim 1, further comprising:

calculating a group affinity score for the member based on interactions of the member related to job searches or job applications for a plurality of companies.

11. A system comprising:

a memory comprising instructions; and

one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising:

identifying jobs based on a search for jobs for a member of a social network, the member working for an employer, each job being associated with a respective company;

for each company associated with one or more of the jobs, determining an intercompany migration score indicating a transition probability that a coworker in working for the employer transitions to work for the company;

for each job, determining a job affinity score based on a comparison of data of the job and a profile of the member;

ranking the jobs based on the intercompany migration score of the company of the job and the job affinity score; and

causing presentation of a group including one or more of the ranked jobs in a user interface of the member based on the ranking.

12. The system as recited inclaim 11, wherein the instructions further cause the one or more computer processors to perform operations comprising:

13. The system as recited inclaim 12, wherein the instructions further cause the one or more computer processors to perform operations comprising:

14. The system as recited inclaim 13, wherein determining the intercompany migration score between the employer and the company is based on the inbound probability and the outbound probability.

15. The system as recited inclaim 11, wherein determining the job affinity score is performed by a machine-learning program based on the data of the job and the profile of the member, the machine-learning program being trained utilizing data of job postings in the social network and data of members of the social network.

16. A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:

for each company associated with one or more of the jobs, determining an intercompany migration score indicating a transition probability that a coworker working for the employer transitions to work for the company;

causing presentation of a group including or more of the ranked jobs in a user interface of the member based on the ranking.

17. The machine-readable storage medium as recited inclaim 16, wherein the machine further performs operations comprising:

18. The machine-readable storage medium as recited inclaim 17, wherein the machine further performs operations comprising:

19. The machine-readable storage medium as recited inclaim 18, wherein determining the intercompany migration score between the employer and the company is based on the inbound probability and the outbound probability.

20. The machine-readable storage medium as recited inclaim 16, wherein determining the job affinity score is performed by a machine-learning program based on the data of the job and the profile of the member, the machine-learning program being trained utilizing data of job postings in the social network and data of members of the social network.